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from __future__ import print_function, division
import numpy as np
import copy
class Neuroevolution():
""" Evolutionary optimization of Neural Networks.
n_individuals: int
The number of neural networks that are allowed in the population at a time.
mutation_rate: float
The probability that a weight will be mutated.
model_builder: method
A method which returns a user specified NeuralNetwork instance.
def __init__(self, population_size, mutation_rate, model_builder):
self.population_size = population_size
self.mutation_rate = mutation_rate
self.model_builder = model_builder
def _build_model(self, id):
""" Returns a new individual """
model = self.model_builder(n_inputs=self.X.shape[1], n_outputs=self.y.shape[1]) = id = 0
model.accuracy = 0
return model
def _initialize_population(self):
""" Initialization of the neural networks forming the population"""
self.population = []
for _ in range(self.population_size):
model = self._build_model(id=np.random.randint(1000))
def _mutate(self, individual, var=1):
""" Add zero mean gaussian noise to the layer weights with probability mutation_rate """
for layer in individual.layers:
if hasattr(layer, 'W'):
# Mutation of weight with probability self.mutation_rate
mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=layer.W.shape)
layer.W += np.random.normal(loc=0, scale=var, size=layer.W.shape) * mutation_mask
mutation_mask = np.random.binomial(1, p=self.mutation_rate, size=layer.w0.shape)
layer.w0 += np.random.normal(loc=0, scale=var, size=layer.w0.shape) * mutation_mask
return individual
def _inherit_weights(self, child, parent):
""" Copies the weights from parent to child """
for i in range(len(child.layers)):
if hasattr(child.layers[i], 'W'):
# The child inherits both weights W and bias weights w0
child.layers[i].W = parent.layers[i].W.copy()
child.layers[i].w0 = parent.layers[i].w0.copy()
def _crossover(self, parent1, parent2):
""" Performs crossover between the neurons in parent1 and parent2 to form offspring """
child1 = self._build_model(
self._inherit_weights(child1, parent1)
child2 = self._build_model(
self._inherit_weights(child2, parent2)
# Perform crossover
for i in range(len(child1.layers)):
if hasattr(child1.layers[i], 'W'):
n_neurons = child1.layers[i].W.shape[1]
# Perform crossover between the individuals' neuron weights
cutoff = np.random.randint(0, n_neurons)
child1.layers[i].W[:, cutoff:] = parent2.layers[i].W[:, cutoff:].copy()
child1.layers[i].w0[:, cutoff:] = parent2.layers[i].w0[:, cutoff:].copy()
child2.layers[i].W[:, cutoff:] = parent1.layers[i].W[:, cutoff:].copy()
child2.layers[i].w0[:, cutoff:] = parent1.layers[i].w0[:, cutoff:].copy()
return child1, child2
def _calculate_fitness(self):
""" Evaluate the NNs on the test set to get fitness scores """
for individual in self.population:
loss, acc = individual.test_on_batch(self.X, self.y) = 1 / (loss + 1e-8)
individual.accuracy = acc
def evolve(self, X, y, n_generations):
""" Will evolve the population for n_generations based on dataset X and labels y"""
self.X, self.y = X, y
# The 40% highest fittest individuals will be selected for the next generation
n_winners = int(self.population_size * 0.4)
# The fittest 60% of the population will be selected as parents to form offspring
n_parents = self.population_size - n_winners
for epoch in range(n_generations):
# Determine the fitness of the individuals in the population
# Sort population by fitness
sorted_i = np.argsort([ for model in self.population])[::-1]
self.population = [self.population[i] for i in sorted_i]
# Get the individual with the highest fitness
fittest_individual = self.population[0]
print ("[%d Best Individual - Fitness: %.5f, Accuracy: %.1f%%]" % (epoch,,
# The 'winners' are selected for the next generation
next_population = [self.population[i] for i in range(n_winners)]
total_fitness = np.sum([ for model in self.population])
# The probability that a individual will be selected as a parent is proportionate to its fitness
parent_probabilities = [ / total_fitness for model in self.population]
# Select parents according to probabilities (without replacement to preserve diversity)
parents = np.random.choice(self.population, size=n_parents, p=parent_probabilities, replace=False)
for i in np.arange(0, len(parents), 2):
# Perform crossover to produce offspring
child1, child2 = self._crossover(parents[i], parents[i+1])
# Save mutated offspring for next population
next_population += [self._mutate(child1), self._mutate(child2)]
self.population = next_population
return fittest_individual
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